Systems and methods for determining contextual rules

Abstract

Systems and methods for determining contextual rules are described herein. In some embodiments, an apparatus may identify a context datum and an interaction datum as a function of a user datum. In some embodiments, an apparatus may determine an interaction feature and a reaction datum as a function of an interaction datum. In some embodiments, an apparatus may determine a contextual rule as a function of the context datum, interaction feature, and reaction datum. In some embodiments, an apparatus may display a visual element to a user as a function of a contextual rule.

What is claimed is: 
     
       1. An apparatus for determining a contextual rule, the apparatus comprising:
 at least a processor; and 
 a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to:
 receiving a user datum, wherein the user datum comprises a plurality of data elements describing at least one planned interaction; 
 identify a context datum by processing the received user datum using a plurality of processing models, wherein identifying the context datum comprises:
 determine at least two identical data elements of the plurality of data elements using the plurality of processing model; and 
 identify the context datum as a function of the at least two identical data elements of the plurality of data elements; 
 
 identify a first interaction datum; 
 determine a first interaction feature as a function of the first interaction datum, using an interaction machine learning model; 
 determine a first reaction datum as a function of the first interaction datum, using a reaction machine learning model; 
 determine a contextual rule as a function of the context datum, the first interaction feature, and the first reaction datum; and 
 determine a visual element of a visual element data structure as a function of the data structure, wherein the visual element data structure comprises:
 a plurality of rules governing timing of a display and formatting of the visual element; and 
 a degree of confidence related to the contextual rule; and 
 
 display the visual element to a user by formatting the visual element based on a rule of the plurality of rules related to the degree of confidence. 
 
 
     
     
       2. The apparatus of  claim 1 , wherein identifying a first interaction datum comprises:
 identifying a first interaction sequence; and 
 identifying a first interaction datum as a single communication from a single communicator in the first interaction sequence. 
 
     
     
       3. The apparatus of  claim 1 , wherein the first interaction datum comprises a recording of human speech. 
     
     
       4. The apparatus of  claim 1 , wherein the interaction machine learning model is configured to categorize inputs into discrete categories. 
     
     
       5. The apparatus of  claim 1 , wherein the reaction machine learning model is configured to output a datum on a continuous scale. 
     
     
       6. The apparatus of  claim 1 , wherein the memory contains instructions configuring the at least processor to:
 identify a second interaction datum; 
 determine a second interaction feature as a function of the second interaction datum, using the interaction machine learning model; 
 determine a second reaction datum as a function of the second interaction datum, using the reaction machine learning model; and 
 determine a contextual rule as a function of the context datum, the first interaction feature, the second interaction feature, the first reaction datum, and the second reaction datum. 
 
     
     
       7. The apparatus of  claim 6 , wherein identifying a second interaction datum comprises:
 identifying a second interaction sequence; and 
 identifying a second interaction datum as a single communication from a single communicator in the second interaction sequence. 
 
     
     
       8. The apparatus of  claim 6 , wherein the first interaction datum and the second interaction datum each comprise a recording of human speech. 
     
     
       9. A method of determining a contextual rule, the method comprising:
 using at least a processor, receiving a user datum comprising a plurality of data elements describing at least one planned interaction; 
 using at least a processor, identifying a context datum by processing the received user datum using a plurality of processing models, wherein identifying the context datum comprises:
 determining at least two identical data elements of the plurality of data elements using the plurality of processing model; and 
 identifying the context datum as a function of the at least two identical data elements of the plurality of data elements; 
 
 using the at least a processor, identifying a first interaction datum; 
 using the at least a processor, determining a first interaction feature as a function of the first interaction datum, using an interaction machine learning model; 
 using the at least a processor, determining a first reaction datum as a function of the first interaction datum, using a reaction machine learning model; 
 using the at least a processor, determining a contextual rule as a function of the context datum, the first interaction feature, and the first reaction datum; and 
 using the at least a processor, determining a visual element of a visual element data structure as a function of the data structure, wherein the visual element data structure comprises:
 a plurality of rules governing timing of a display and formatting of the visual element; and 
 a degree of confidence related to the contextual rule; and 
 
 using the at least a processor, displaying the visual element to a user by formatting the visual element based on a rule of the plurality of rules related to the degree of confidence. 
 
     
     
       10. The method of  claim 9 , wherein identifying a first interaction datum comprises:
 identifying a first interaction sequence; and 
 identifying a first interaction datum as a single communication from a single communicator in the first interaction sequence. 
 
     
     
       11. The method of  claim 9 , wherein the first interaction datum comprises a recording of human speech. 
     
     
       12. The method of  claim 9 , wherein the interaction machine learning model is configured to categorize inputs into discrete categories. 
     
     
       13. The method of  claim 9 , wherein the reaction machine learning model is configured to output a datum on a continuous scale. 
     
     
       14. The method of  claim 9 , further comprising:
 identifying a second interaction datum; 
 determining a second interaction feature as a function of the second interaction datum, using the interaction machine learning model; 
 determining a second reaction datum as a function of the second interaction datum, using the reaction machine learning model; and 
 determining a contextual rule as a function of the context datum, the first interaction feature, the second interaction feature, the first reaction datum, and the second reaction datum. 
 
     
     
       15. The method of  claim 14 , wherein identifying a second interaction datum comprises:
 identifying a second interaction sequence; and 
 identifying a second interaction datum as a single communication from a single communicator in the second interaction sequence. 
 
     
     
       16. The method of  claim 14 , wherein the first interaction datum and the second interaction datum each comprise a recording of human speech.